Construction of a Bayesian inversion method for determining seafloor sediment properties from simulated transmission loss data

2020 ◽  
Vol 148 (4) ◽  
pp. 2730-2730
Author(s):  
James Albritton ◽  
Aaron Gunderson
Keyword(s):  
Transmission Loss ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Sediment Properties ◽  
Loss Data ◽  
Seafloor Sediment

scholarly journals Estimates of European emissions of methyl chloroform using a Bayesian inversion method

2014 ◽  
Vol 14 (18) ◽  
pp. 9755-9770 ◽  
Author(s):  
M. Maione ◽  
F. Graziosi ◽  
J. Arduini ◽  
F. Furlani ◽  
U. Giostra ◽  
...  
Keyword(s):  
Point Source ◽  
High Frequency ◽  
Stratospheric Ozone ◽  
Montreal Protocol ◽  
Source Analysis ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Data Set ◽  
Methyl Chloroform

Abstract. Methyl chloroform (MCF) is a man-made chlorinated solvent contributing to the destruction of stratospheric ozone and is controlled under the "Montreal Protocol on Substances that Deplete the Ozone Layer" and its amendments, which called for its phase-out in 1996 in developed countries and 2015 in developing countries. Long-term, high-frequency observations of MCF carried out at three European sites show a constant decline in the background mixing ratios of MCF. However, we observe persistent non-negligible mixing ratio enhancements of MCF in pollution episodes, suggesting unexpectedly high ongoing emissions in Europe. In order to identify the source regions and to give an estimate of the magnitude of such emissions, we have used a Bayesian inversion method and a point source analysis, based on high-frequency long-term observations at the three European sites. The inversion identified southeastern France (SEF) as a region with enhanced MCF emissions. This estimate was confirmed by the point source analysis. We performed this analysis using an 11-year data set, from January 2002 to December 2012. Overall, emissions estimated for the European study domain decreased nearly exponentially from 1.1 Gg yr−1 in 2002 to 0.32 Gg yr−1 in 2012, of which the estimated emissions from the SEF region accounted for 0.49 Gg yr−1 in 2002 and 0.20 Gg yr−1 in 2012. The European estimates are a significant fraction of the total semi-hemisphere (30–90° N) emissions, contributing a minimum of 9.8% in 2004 and a maximum of 33.7% in 2011, of which on average 50% are from the SEF region. On the global scale, the SEF region is thus responsible for a minimum of 2.6% (in 2003) and a maximum of 10.3% (in 2009) of the global MCF emissions.

BAYESIAN MAGNETOTELLURIC INVERSION USING METHYLENE BLUE STRUCTURAL PRIORS FOR IMAGING SHALLOW CONDUCTORS IN GEOTHERMAL FIELDS

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Alberto Ardid ◽  
David Dempsey ◽  
Edward Bertrand ◽  
Fabian Sepulveda ◽  
Flora Solon ◽  
...  
Keyword(s):  
Methylene Blue ◽  
Clay Content ◽  
3D Models ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Geothermal Fields ◽  
Geological Information ◽  
Resistivity Model ◽  
Irreducible Uncertainty ◽  
Clay Cap

In geothermal exploration, magnetotelluric (MT) data and inversion models are commonly used to image shallow conductors typically associated with the presence of an electrically conductive clay cap that overlies the main reservoir. However, these inversion models suffer from non-uniqueness and uncertainty, and the inclusion of useful geological information is still limited. We develop a Bayesian inversion method that integrates the electrical resistivity distribution from MT surveys with borehole methylene blue data (MeB), an indicator of conductive clay content. MeB data is used to inform structural priors for the MT Bayesian inversion that focus on inferring with uncertainty the shallow conductor boundary in geothermal fields. By incorporating borehole information, our inversion reduces non-uniqueness and then explicitly represents the irreducible uncertainty as estimated depth intervals for the conductor boundary. We use Markov chain Monte Carlo (McMC) and a one-dimensional three-layer resistivity model to accelerate the Bayesian inversion of the MT signal beneath each station. Then, inferred conductor boundary distributions are interpolated to construct pseudo-2D/3D models of the uncertain conductor geometry. We compared our approach against a deterministic MT inversion software on synthetic and field examples and showed good performance in estimating the depth to the bottom of the conductor, a valuable target in geothermal reservoir exploration.

scholarly journals Evaluation of a Straight-Ray Forward Model for Bayesian Inversion of Crosshole Ground Penetrating Radar Data

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 630 ◽  
Author(s):  
Hui Qin ◽  
Xiongyao Xie ◽  
Yu Tang
Keyword(s):  
Ground Penetrating Radar ◽  
Computational Cost ◽  
Measurement Data ◽  
Forward Model ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Error ◽  
Ground Penetrating

Bayesian inversion of crosshole ground penetrating radar (GPR) data is capable of characterizing the subsurface dielectric properties and qualifying the associated uncertainties. Markov chain Monte Carlo (MCMC) simulations within the Bayesian inversion usually require thousands to millions of forward model evaluations for the parameters to hit their posterior distributions. Therefore, the CPU cost of the forward model is a key issue that influences the efficiency of the Bayesian inversion method. In this paper we implement a widely used straight-ray forward model within our Bayesian inversion framework. Based on a synthetic unit square relative permittivity model, we simulate the crosshole GPR first-arrival traveltime data using the finite-difference time-domain (FDTD) and straight-ray solver, respectively, and find that the straight-ray simulator runs 450 times faster than its FDTD counterpart, yet suffers from a modeling error that is more than 7 times larger. We also perform a series of numerical experiments to evaluate the performance of the straight-ray model within the Bayesian inversion framework. With modeling error disregarded, the inverted posterior models fit the measurement data nicely, yet converge to the wrong set of parameters at the expense of unreasonably large number of iterations. When the modeling error is accounted for, with a quarter of the computational burden, the main features of the true model can be identified from the posterior realizations although there still exist some unwanted artifacts. Finally, a smooth constraint on the model structure improves the inversion results considerably, to the extent that it enhances the inversion accuracy approximating to those of the FDTD model, and further reduces the CPU demand. Our results demonstrate that the use of the straight-ray forward model in the Bayesian inversion saves computational cost tremendously, and the modeling error correction together with the model structure constraint are the necessary amendments that ensure that the model parameters converge correctly.

scholarly journals European emissions of HCFC-22 based on eleven years of high frequency atmospheric measurements and a Bayesian inversion method

2015 ◽  
Vol 112 ◽  
pp. 196-207 ◽  
Author(s):  
F. Graziosi ◽  
J. Arduini ◽  
F. Furlani ◽  
U. Giostra ◽  
L.J.M. Kuijpers ◽  
...  
Keyword(s):  
High Frequency ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Atmospheric Measurements
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